Until now the operating conditions of a machine or an installation were evaluated using visual extrapolations of a, for example, critical measurement value pattern and/or subjective evaluations of the impact on parameters of the machine or the installation, in order to estimate the operating conditions of a machine or an installation and to respond accordingly by changing or otherwise influencing the parameter according to the evaluation, for example by predetermining a target value for parameters.
In this process adjustment/regression functions of a database were adjusted and an optimization process was carried out by iterative selection of curve functions, with the maximum correlation coefficient. The curvature pattern of such an adjusted curve does not necessarily have to correspond to that of the database. The correlation coefficient r (maximum value=1, minimum value=−1) can only be used as an adjustment quality criterion under certain conditions, as this value depends not only on the adjustment quality of the curve function used but also on the gradient of the curve function used. If the gradient, for example, of a linear adjustment tends toward zero, r also follows this trend, regardless of the scatter of the individual curve points.
This means that r cannot be used as a measure of quality for an extrapolation.
Generation tools for evaluating the operating conditions of a machine or an installation have to satisfy certain minimum requirements, so that an evaluation of the extrapolation result is possible in respect of                Predictive reliability        Variables influencing predictive dependability        Traceability of the prediction.        
Naturally any prediction is subject to uncertainty and its measure of quality for assessing forecast alarms/exceeded limit values and as a basis for decision for resulting, e.g. automated, actions is extremely important. In addition, in the case of a cyclically generated method for evaluating the operating conditions of a machine or an installation in a specific, e.g. measurement context, the change in a measure of quality over time can also be seen as a trend and can therefore also offer additional conclusions about the time of occurrence of the forecast event, e.g. the exceeding of a limit value, in order to ensure the operational dependability of the machine or the installation.
Estimating the reliability of an extrapolation is of central significance, as evaluations of damage symptoms, the exceeding of limit values and operational optimization, e.g. replacement of parts is not really possible without knowledge of the predictive reliability, in other words the measure of quality.